devtools::install_github("dionnecargy/pvsero") # To download the package
library(pvsero) # To load the package
library(tidyverse) # for data wrangling and visualisation
library(knitr) # for RMarkdown visualisation and PDF generationTutorials
Load the Package and Data
Data Analysis: runPvSeroPipeline()
Run this global function runPvSeroPipeline() embedded within the {pvsero} R package! This function contains all of the steps in order of how to perform the Plasmodium vivax serology test and treat protocol as found in our application!
Using Tutorial Dataset: Load the Data
We will be using the build-in files in the R package for this tutorial, as shown below.
your_raw_data <- c(
system.file("extdata", "example_MAGPIX_plate1.csv", package = "pvsero"),
system.file("extdata", "example_MAGPIX_plate2.csv", package = "pvsero"),
system.file("extdata", "example_MAGPIX_plate3.csv", package = "pvsero")
)
your_plate_layout <- system.file("extdata", "example_platelayout_1.xlsx", package = "pvsero")To run your OWN data, follow the code below and uncomment (i.e., remove the hashtags):
# your_raw_data <- c(
# "PATH/TO/YOUR/FILE/plate1.csv",
# "PATH/TO/YOUR/FILE/plate2.csv",
# "PATH/TO/YOUR/FILE/plate3.csv"
# )
# your_plate_layout <- "PATH/TO/YOUR/FILE/plate_layout.xlsx"Run Classification: Yes
final_analysis <- runPvSeroPipeline(
raw_data = your_raw_data,
plate_layout = your_plate_layout,
platform = "magpix",
location = "ETH",
experiment_name = "experiment1",
classify = "Yes",
algorithm_type = "antibody_model",
sens_spec = "maximised"
)Classification
This is a table containing the classification results (seropositive or seronegative) for each SampleID. In this case, the classification results are stored in the pred_class_max column as we chose the sens_spec = "maximised". If you change it to another type of threshold, then the suffix of that column will change accordingly.
You will also see the relative antibody unit (RAU) values (columns with antigen names), whether the sample passed QC check (QC_total) and the plate that they were run on.
final_analysis[[1]] %>%
head() %>%
kable()| SampleID | Plate | QC_total | EBP | LF005 | LF010 | LF016 | MSP8 | PTEX150 | PvCSS | RBP2b.P87 | pred_class_max |
|---|---|---|---|---|---|---|---|---|---|---|---|
| ABC013 | plate1 | pass | 0.0003339 | 0.0015045 | 0.0002163 | 0.0014567 | 0.0000195 | 0.0001591 | 0.0000772 | 0.0003714 | seropositive |
| ABC097 | plate2 | pass | 0.0004324 | 0.0015615 | 0.0001944 | 0.0013373 | 0.0000195 | 0.0001549 | 0.0000705 | 0.0009189 | seropositive |
| ABC181 | plate3 | pass | 0.0003822 | 0.0015832 | 0.0002144 | 0.0013711 | 0.0000195 | 0.0001582 | 0.0000710 | 0.0002070 | seropositive |
| ABC022 | plate1 | pass | 0.0200000 | 0.0200000 | 0.0007373 | 0.0194885 | 0.0006247 | 0.0003145 | 0.0006008 | 0.0004895 | seropositive |
| ABC106 | plate2 | pass | 0.0057123 | 0.0193731 | 0.0007458 | 0.0195240 | 0.0006263 | 0.0003077 | 0.0006480 | 0.0126203 | seropositive |
| ABC190 | plate3 | pass | 0.0098260 | 0.0200000 | 0.0007400 | 0.0200000 | 0.0006020 | 0.0003171 | 0.0006555 | 0.0175253 | seropositive |
Standard Curve Plot
The standard curve plots are generated from the antibody data from the standards you indicated in your plate layout (e.g. S1-S10) and Median Fluorescent Intensity (MFI) units are displayed in log10-scale. In the case of the PvSeroTaT multi-antigen panel, the antigens will be displayed and in general your standard curves should look relatively linear (only when the y-axis is on logarithmic scale).
final_analysis[[2]]
Bead Counts QC Plot
A summary of the bead counts for each plate well are displayed, with blue indicating there are sufficient beads (≥15) or red when there are not enough. If any of the wells are red, they should be double-checked manually and re-run on a new plate if required.
The function will inform you whether there are “No repeats necessary” or provide a list of samples to be re-run. In the example data, the beads in plate 2 wells A1 and A2 will need to be repeated
final_analysis[[3]] # Plot
final_analysis[[4]] # Samples to repeat # A tibble: 2 × 4
Location SampleID Plate QC
<chr> <chr> <chr> <chr>
1 A1 Blank1 plate2 fail
2 A2 Blank2 plate2 fail Blanks QC Plot
The Median Fluorescent Intensity (MFI) units for each antigen is displayed for your blank samples. In general, each blank sample should have ≤50 MFI for each antigen, if they are higher they should be cross-checked manually.
In the example data, blank samples recorded higher MFI values for LF005 on plate 1 and should be checked to confirm this is expected from the assay.
final_analysis[[5]]
Model Output Plot
The automated data processing in this app allows you to convert your Median Fluorescent Intensity (MFI) data into Relative Antibody Units (RAU) by fitting a 5-parameter logistic function to the standard curve on a per-antigen level. The results from this log-log conversion should look relatively linear for each antigen.
final_analysis[[6]]$plate1
$plate2
$plate3
Run Classification: No
no_classification_final_analysis <- runPvSeroPipeline(
raw_data = your_raw_data,
plate_layout = your_plate_layout,
platform = "magpix",
location = "ETH",
experiment_name = "experiment1",
classify = "No", ########################## key if you do NOT want any classification performed i.e., you do not have PvSeroTaT antigens
algorithm_type = "antibody_model",
sens_spec = "maximised"
)MFI and RAU Data
no_classification_final_analysis[[1]] %>%
head() %>%
kable()| SampleID | Plate | QC_total | EBP_MFI | EBP_Dilution | LF005_MFI | LF005_Dilution | LF010_MFI | LF010_Dilution | LF016_MFI | LF016_Dilution | MSP8_MFI | MSP8_Dilution | PTEX150_MFI | PTEX150_Dilution | PvCSS_MFI | PvCSS_Dilution | RBP2b.P87_MFI | RBP2b.P87_Dilution |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ABC013 | plate1 | pass | 2712 | 0.0003339 | 1569.0 | 0.0015045 | 673 | 0.0002163 | 327 | 0.0014567 | 182.0 | 1.95e-05 | 936.0 | 0.0001591 | 223 | 0.0000772 | 1068.0 | 0.0003714 |
| ABC014 | plate1 | pass | 134 | 0.0000216 | 378.0 | 0.0004204 | 117 | 0.0000277 | 197 | 0.0010331 | 58.0 | 1.95e-05 | 122.0 | 0.0000225 | 93 | 0.0000298 | 465.5 | 0.0001536 |
| ABC015 | plate1 | pass | 182 | 0.0000232 | 209.0 | 0.0002466 | 208 | 0.0000616 | 374 | 0.0015985 | 221.5 | 1.95e-05 | 293.0 | 0.0000535 | 868 | 0.0002235 | 463.0 | 0.0001527 |
| ABC016 | plate1 | pass | 152 | 0.0000222 | 229.5 | 0.0002688 | 101 | 0.0000244 | 89 | 0.0005994 | 48.0 | 1.95e-05 | 109.0 | 0.0000220 | 110 | 0.0000383 | 591.0 | 0.0001989 |
| ABC017 | plate1 | pass | 1135 | 0.0001478 | 236.0 | 0.0002758 | 299 | 0.0000950 | 507 | 0.0019840 | 209.5 | 1.95e-05 | 1665.5 | 0.0002668 | 266 | 0.0000893 | 671.0 | 0.0002277 |
| ABC018 | plate1 | pass | 174 | 0.0000229 | 395.0 | 0.0004370 | 175 | 0.0000460 | 78 | 0.0005452 | 70.0 | 1.95e-05 | 294.5 | 0.0000537 | 92 | 0.0000296 | 103.0 | 0.0000238 |
QC Plots
Repeat the same steps as above to find the QC plots!
#### Standard Curve Plot
no_classification_final_analysis[[2]]
#### Bead Counts QC Plot
no_classification_final_analysis[[3]] # Plot
no_classification_final_analysis[[4]] # Samples to repeat # A tibble: 2 × 4
Location SampleID Plate QC
<chr> <chr> <chr> <chr>
1 A1 Blank1 plate2 fail
2 A2 Blank2 plate2 fail #### Blanks QC Plot
no_classification_final_analysis[[5]]
#### Model Output Plot
no_classification_final_analysis[[6]]$plate1
$plate2
$plate3
Create a PDF Report
renderQCReport(
your_raw_data,
your_plate_layout,
"magpix",
location = "ETH"
)
|
| | 0%
|
|... | 7%
|
|...... | 13% [setup]
|
|......... | 20%
|
|............. | 27% [standard curves plot]
|
|................ | 33%
|
|................... | 40% [model results plot]
|
|...................... | 47%
|
|......................... | 53% [bead counts plot]
|
|............................ | 60%
|
|............................... | 67% [check repeats table]
|
|.................................. | 73%
|
|...................................... | 80% [blank samples plot]
|
|......................................... | 87%
|
|............................................ | 93% [plate layouts]
|
|...............................................| 100%
/Applications/RStudio.app/Contents/Resources/app/quarto/bin/tools/x86_64/pandoc +RTS -K512m -RTS template.knit.md --to latex --from markdown+autolink_bare_uris+tex_math_single_backslash --output /Users/Dionne/Documents/GitHub/pvsero/experiment1_20250730_ETH_v1.3.1_QCreport.tex --lua-filter /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/library/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --highlight-style tango --pdf-engine pdflatex --variable graphics --include-in-header /var/folders/bh/0yzt0_x97vj_zktb_39c1xvh0000gn/T//Rtmpa3jx0w/rmarkdown-str16e8e2962b55a.html --variable 'geometry:margin=1in' --include-in-header /var/folders/bh/0yzt0_x97vj_zktb_39c1xvh0000gn/T//Rtmpa3jx0w/rmarkdown-str16e8ef703cf0.html Data Analysis: Pk/Pv/Pf
5-Point Standard Curve
Step 1: Load your data!
Firstly, we will be using our example data that’s in-built in the package. Here replace the system.file() argument with the file path for your package.
your_raw_data_5std <- c(
system.file("extdata", "example_MAGPIX_pk_5std_plate1.csv", package = "pvsero"),
system.file("extdata", "example_MAGPIX_pk_5std_plate2.csv", package = "pvsero")
)
your_plate_layout_5std <- system.file("extdata", "example_platelayout_pk_5std.xlsx", package = "pvsero")Step 2: Read your data and process MFI to RAU
Caitlin and Dionne have worked on a function to (a) process raw Serological data and (b) convert MFI to RAU. The runPlasmoSero5point() function will output three data frames: (i) Results from MAGPIX (MFI), (ii), Counts for each sample, (iii) Processed RAU values for each sample. These dataframes can be used for further analysis and can be transformed as you wish.
pk_analysis_1 <- runPlasmoSero5point(
raw_data = your_raw_data_5std,
platform = "magpix",
plate_layout = your_plate_layout_5std,
date = "2025-07-30" # Optional: This will default to today's date
)Standard Curve Plot: One Curve
As requested, this is a plot of ONE standard curve which you will specify. You can modify this plot as you see fit by piping other ggplot2() arguments.
plotOneStdCurve(pk_analysis_1, "plate1")
Standard Curve Plot: Compare Two Curves
This function allows you to plot all of your standard curves.
plotManyStdCurves(pk_analysis_1)
10-Point Standard Curve
These steps are very similar to the 5-point standard curve, except here we use the runPlasmoSero10point() function.
Step 1: Load your data!
your_raw_data_10std <- c(
system.file("extdata", "example_MAGPIX_pk_10std_plate1.csv", package = "pvsero"),
system.file("extdata", "example_MAGPIX_pk_10std_plate2.csv", package = "pvsero")
)
your_plate_layout_10std <- system.file("extdata", "example_platelayout_pk_10std.xlsx", package = "pvsero")Step 2: Read your data and process MFI to RAU
pk_analysis_2 <- runPlasmoSero10point(
raw_data = your_raw_data_10std,
platform = "magpix",
plate_layout = your_plate_layout_10std,
date = "2025-07-30" # Optional: This will default to today's date
)Standard Curve Plot: One Curve
plotOneStdCurve(pk_analysis_2, "plate1")
Standard Curve Plot: Compare Two Curves
plotManyStdCurves(pk_analysis_2)
Visualisation of the {pvsero} R Package
We have used the {targets} R package to generate a pipeline! This allows us to:
- Automatically detect the dependencies of each step
- One-command execution
- Automatic caching
- Automatic detection of changes in data and/or code
For more information on {targets} see this tutorial.
here() starts at /Users/Dionne/Documents/GitHub/pvsero
✔ skipped pipeline [39ms, 15 skipped]
Warning message:
package ‘targets’ was built under R version 4.4.1 here() starts at /Users/Dionne/Documents/GitHub/pvsero
Warning message:
package ‘targets’ was built under R version 4.4.1